BackgroundThe success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.ResultsAverage r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.ConclusionsOur estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

Pigs from the F(2) generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting carcass composition and meat quality traits. Carcass composition phenotypes included primal cut weights, skeletal characteristics, backfat thickness, and LM area. Meat quality data included LM pH, temperature, objective and subjective color information, marbling and firmness scores, and drip loss. Additionally, chops were analyzed for moisture, protein, and fat composition as well as cook yield and Warner-Bratzler shear force measurements. Palatability of chops was determined by a trained sensory panel. A total of 510 F(2) animals were genotyped for 124 microsatellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression interval, mapping methods using sex and litter as fixed effects and carcass weight or slaughter age as covariates. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined on chromosome- and genome-wise levels by permutation tests. A total of 94 QTL for 35 of the 38 traits analyzed were found to be significant at the 5% chromosome-wise level. Of these 94 QTL, 44 were significant at the 1% chromosome-wise, 28 of these 44 were also significant at the 5% genome-wise, and 14 of these 28 were also significant at the 1% genome-wise significance thresholds. Putative QTL were discovered for 45-min pH and pH decline from 45 min to 24 h on SSC 3, marbling score and carcass backfat on SSC 6, carcass length and number of ribs on SSC 7, marbling score on SSC 12, and color measurements and tenderness score on SSC 15. These results will facilitate fine mapping efforts to identify genes controlling carcass composition and meat quality traits that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations.

BackgroundCurrently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects.ResultsThe standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest − log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance.ConclusionsThe standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-246) contains supplementary material, which is available to authorized users.

The length of adult sow life is now recognized as both an economic and a welfare concern. However, there are no consistent definitions to measure sow longevity. This study assessed 6 different descriptions of longevity and determined their relationship with developmental performance factors. Longevity definitions included stayability (probability of a sow producing 40 pigs or probability of her reaching 4 parities), lifespan (number of parities a female has accumulated before culling), lifetime prolificacy (number of pigs born alive during the productive lifetime of a female), herd life (time from first farrowing to culling), and pigs produced per day of life. Data consisted of 14,262 records of Yorkshire females from both nucleus and multiplication herds across 21 farms from 4 seedstock systems. Within a subset of the data, information was available on the litter birth record of the female and her growth and composition data. Therefore, data were subdivided into 2 data sets, consisting of 1) data A, data from the farrowing records of a female, and 2) data B, data A and information from the litter birth record of a female and the growth and backfat data from a female. A Cox proportional hazards model was used to determine the relationship of developmental factors and first farrowing record with longevity. Those factors that were significantly (P < 0.0001) associated with longevity, regardless of definition, were age at first farrowing, litter size at first farrowing and last farrowing, number of stillborn in the first litter, adjusted 21-d litter weight of the first litter, herd type, backfat, and growth. Within a contemporary group, fatter, slower growing gilts had a decreased risk of being culled. Additionally, sows that had more pigs born alive, fewer stillborn pigs, and heavier litters at 21 d of lactation in their first litter had a decreased risk of being culled. Furthermore, sows from nucleus herds experienced a greater risk of being culled. Many factors affected longevity, regardless of definition. Pork producers can implement management protocols that can extend the productive life of breeding females, resulting in improved profitability and animal welfare.

Pigs from the F(2) generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting growth and composition traits. Body weight and ultrasound estimates of 10th-rib backfat, last-rib backfat, and LM area were serially measured throughout development. Estimates of fat-free total lean, total body fat, empty body protein, empty body lipid, and ADG from 10 to 22 wk of age were calculated, and random regression analyses were performed to estimate individual animal phenotypes representing intercept and linear rates of increase in these serial traits. A total of 510 F(2) animals were genotyped for 124 micro-satellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression, interval mapping methods using sex and litter as fixed effects. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined at the chromosome- and genome-wise levels by permutation tests. A total of 43 QTL for 22 of the 29 measured traits were found to be significant at the 5% chromosome-wise level. Of these 43 QTL, 20 were significant at the 1% chromosome-wise significance threshold, 14 of these 20 were also significant at the 5% genome-wise significance threshold, and 10 of these 14 were also significant at the 1% genome-wise significance threshold. A total of 22 QTL for the animal random regression terms were found to be significant at the 5% chromosome-wise level. Of these 22 QTL, 6 were significant at the 1% chromosome-wise significance threshold, 4 of these 6 were also significant at the 5% genome-wise significance threshold, and 3 of these 4 were also significant at the 1% genome-wise significance threshold. Putative QTL were discovered for 10th-rib and last-rib backfat on SSC 6, body composition traits on SSC 9, backfat and lipid composition traits on SSC 11, 10th-rib backfat and total body fat tissue on SSC 12, and linear regression of last-rib backfat and total body fat tissue on SSC 8. These results will facilitate fine-mapping efforts to identify genes controlling growth and body composition of pigs that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations.